EVALUVATING road traffic accident is any harm due

EVALUVATING
ROAD TRAFFIC ACCIDENTS USING DATA

MINING
TECHNOLOGY

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ABSTRACT:-

Road
traffic safety is an important perturbation for government transport
authorities as well as common people. Road accidents are ambivalent and not
able to be predict the incidents. And their survey requires the information
affecting them. Road accidents cause difficulties which are get bigger at an
alarming rate. Controlling the traffic accidents on roads is a crucial task. To
give safe driving suggestions, clear and careful study of roadway traffic data
is critical to find out the variables that are nearly to fatal accidents.
Increasing the number of vehicles from past few years has put lot of pressure
on the existing roads and ultimately resulting in increasing the road
accidents. A road traffic accident is any harm due to collision originating
from, terminating with or involving a vehicle partially or fully on a public
road.

I. INTRODUCTION:-

            In modern life, accidents have
become daily happening. Every day we hear the news of the accident on the
television, or through internet .During accident many people die at the spot, some
others may injured very severely. By witnessing an accident one can understand
the horror of it. There are several reasons for road accidents, some of them are
increasing the number of vehicles, careless driving, violating traffic rules
etc. Whenever a road accident occur there are various types of damage takes
place ,which could be in the form of human beings, infrastructure which is
damage to the government and many other administration damages . Poor roadway maintenance
also contributes accidents. But still many people continue to neglect and
ignore the danger involved in the accidents. In this paper we are analyzing
some methods and algorithms to find out the problems occur in road accidents.

Section
1 elucidate literature survey,Section 2 elucidate conclusion.

 

 

LITERATURE SURVEY

The paper 1 describes the association
rule mining, its classifications and the atmospheric components like roadway
surface, climate, and light condition do not strongly influence the fatal accident
rate. But the human factors like being alcoholic or not, and the impact have
strongly affect on the fatal accident rate.  A
common mechanism to recognize the relations between the data stored in huge
database and plays a very significant role in repeated object set mining is
association rule mining algorithm. A classical association rule mining method
is the Apriori algorithm whose main aim is to identify repeated object sets to
analyze the roadway traffic data. Classification in data mining methodology focus
at building a classifier model from a training data set that is used to
classify records of unrevealed class labels. The Naïve Bayes technique is one
of the probability-based methods for classification and is based on the Bayes’
hypothesis with the probability of self-rule between every set of variables. The author applies statistics
analysis and Fatal Accident Reporting System (FARS) to solve this problem. From
the clustering result some regions have larger fatal rate but some others have
smaller. When driving within those risky or dangerous states, people take more
attention. When the task performed, data seems never to be sufficient to make a
strong choice. If non-fatal accident data, weather condition data, mileage
data, and so on are available, more test could be executed thus more advice
could be made from the data.

In paper 2, K-modes clustering
technique is a framework that is used as an initial work for division of
different road accidents on road network. Then association rule mining are used
to recognize the various situations that are related with the occurrence of an
accident for the entire data set (EDS) and the clusters recognized by K-modes
clustering algorithm. Six clusters (C1toC6) are used based on properties
accident type, road type, lightning on road and road feature identified by K
modes clustering method. On each cluster association rule mining is applied as
well as on EDS to create rules. Powerful methods with higher raise values are taken
for the inspection. Rules for various clusters disclose the situations related
with the accidents within that cluster. These rules are compared with the rules
created for the EDS and resemblance shows that association rules for EDS does not
disclose correct data that can be related with an accident. If more feature are
presented large information can be identified that is associated with an
accident. To buildup our methodology, we also performed analysis of all clusters
and EDS on monthly or hourly basis. The results of analysis assist methodology
that performing clustering prior to analysis helps to identify better and
useful results that cannot obtained without using cluster analysis.

The paper 3 performs statistical
and empirical analysis on State Highways and Ordinary District Roads accidental
datasets. The need of the study is to analyze the traffic accident data of SH’s
and ODR’s to assign the black spots and accidental elements, part to control
the harm caused by the accidents. The basic necessity of the analysis is to
check the traffic associated dataset through Exploratory Visualization
Techniques, K-means and KNN Algorithms using Rstudio.. The term accident black
spot in management of road traffic safety defines a place where accidents are
been focus historically and to analyze the accidental data using exploratory
visualization techniques and machine learning algorithms. These techniques and
algorithms are used on the traffic accidental dataset to get the desired output
in order to reduce the accident frequency.  Exploratory Visualization Technique is a
technique to anatomize and examine the sets of data in order to abridge and
encapsulate the important characteristics with visual and pictorial method.
Exploratory Visualization analysis can be performed using scatter plot,
correlation analysis, barplot, clustered barplot, histogram, pie chart etc. Machine
learning concentrates on algorithm designing and makes predictions on sets of
data. It includes Supervised (KNN Algorithm) and Unsupervised learning (K-means
Algorithm).This paper present result by resembling the above  three mining techniques and assigns the cause
of accident, accident prone area, analyze the time of accident, examine the
cause of accident and scrutinize the litigators vehicle.

In paper 4, describes
about a frame work that uses K-mode clustering technique as a primary task for
dividing 11574 accidents on road network of Dehradun (India) from 2009 to 2014.
Then an association mining rule are used to find out the various context associated
with instance of an accident for both the whole data set and clusters find out by
K-modes clustering algorithm. Then compare the findings from cluster based
analysis and entire data set. The results shows that the amalgamation of k mode
clustering and association mining rule is very encouraging, as it produces
important facts that would remain hidden if no segmentation has been performed
prior to generate association rules. Also a trend analysis has been performed
on each clusters and entire data set. By trend analysis it shows that before
analysis, prior segmentation of data is very important. This paper put forward
a frame work based on cluster analysis using k-mode algorithm and association
mining rule. By using cluster analysis as a primary task can group the data into
different homogeneous parts. It is the first time that both association and
clustering rule are used together to analyze the data’s for road accidents. The
output of the study proves that by using cluster analysis as a primary task, it
can help in removing heterogeneity to some extent in the road accident data.)
Based on attributes accident type, road type, lightning on road and road feature,
K -modes clustering find six cluster (C1–C6). Association mining rule have been
applied on each cluster as well as on entire data set to generate rules. For
this analysis strong rules with high lift values are used.

The paper 5 describes
purpose of data mining methods in the field of road accident investigation. .
Association rules are used to identify the patterns and rules that are
subjected the cause the occurrence of road accidents. An efficient method for updating
the index year after year could be designed. Additionally, further analysis of
traffic safety data using data mining techniques are allowed. Cluster analysis evaluates data
objects without consulting a common class label. The objects are clustered or
arranged on the basis of maximizing the intra class similarity and minimizing
the interclass similarity. Outlier analysis: A database having data objects
that do not satisfies the general behavior or model of  the data. These data objects are also called outliers.
Evolution analysis which defines and models consistencies or trends for
objects whose behavior changes over time. We are currently build up by
considering several issues, changes in clash occurrence may have some
aftereffect for traffic safety measures in certain countries. The determination
of specific precautionary measures to overcome clashes requires study of other
factors such as the identification of specific road sections that need work,
etc.. It analyzed the traffic accident using data mining technique that could
possibly reduce the fatality rate. Using a road safety database enables to
reduce the fatality by implementing road safety programs at local and national
levels.

            The paper 6 describes data mining
techniques to analyze high-frequency accident locations and further identify
different factors that affect road accidents at specifying locations. We first
partitioned the accident locations into k groups based on their accident
frequency poll using k-means clustering algorithm. Association rule mining algorithm
is used to reveal the correlation between different elements in the accident
data and understand the characteristics of these locations. Hence, the major significance
will be the evaluation of the outcomes. Data mining has been proven as a
reliable technique to analyzing road accident data. Several data mining
techniques such as clustering, classification and association rule mining are
widely used in the literature to identify reasons that affect the severity of
road accidents. It is the first time that k-means algorithm is used to identify high- and
low-frequency accident locations based on accident count as it provides some
technical measures to divide the accident locations based on threshold values. The road accident dataset
and its analysis using k-means clustering and association rule mining algorithm
shows that this approach can be reused on other accident data with more
attributes to identify various other factors associated with road accidents.

In paper 7 describes
the
results from analysis of traffic accidents on the Finnish roads by applying
large scale data mining methods. The set of data collected from road traffic
accidents are vast, multidimensional and diverse. The
Finnish Road Administration between 2004 and 2008 data was collected for this
study. This set of data contain more than 83000 accidents and 1203 of which are
fatal. The main aim of this is to examine the usability of robust
clustering, association and frequent item sets, and visualization methods to
the road traffic accident analysis. The output shows that the pick out data
mining methods are able to produce intelligible patterns from the data,
detecting more information that could be increased with more detailed and
comprehensive data sets. Most of the fatal accidents occur due to
the condition of single roadway main roads outside built-up areas where the
permitted speed varies typically between 80-100km/h. Aged and young drivers have
large contribution to the high risk accidents in highways. Most of the surveys
reported that one of the major reason for accidents among young people are
consumption of alcohol. From the analysis it is understand that failure of
roads and end user groups are responsible for accidents at certain limit.

            This
paper 8 is to represent a Traffic Accident Report and Analysis System (TARAS)
through data mining using Clustering technique. Detect the causes of accidents
is the main aim of this paper. The transport department of government of India
produced the dataset for the study contains traffic accident records of the
year and look into the performance of J48. The classification accuracy on the
test result discloses the three cases such as accident, vehicle and casualty. Genetic
Algorithms is used for the future selection to lower the measurements of the
dataset.. More detailed area specific information from accident locations and circumstances
are needed. With the help of this paper, the analysis can be done and therefore
preventive measures can be taken. It can help the government to keep track of
records of the accidents, causes of accident, vehicle number, vehicle owner’s
name and address.. With the current data it is possible to identify the risky road
segments and the road user groups responsible for accidents in certain environments.
The viewer or user can also make their own account for viewing the site .you
can view the data about causality .Our system will provide the graphical view of
the accidents with respect to the data entered into the system according to the
period .This system will provide the solutions as accidents causes. So that
with the help of this system government can take the necessary actions
according accidents cases.

1) Accurate Location of
accident

2) GPS integration

3) Government ID
Authentication for user Data

4) Advanced Filter
technique Accident Solution prediction.

The paper9 describes application of data mining techniques on road accidents by using
machine learning algorithms that determines accident rate in the future to
decrease clash deaths and wounds. The accident dataset
contains traffic accident report of various cities examined by using machine
learning algorithms to predict the accident rate. It
implemented hybrid approach that performed with higher accuracy rate as
compared to other methods to be described. The machine learning techniques is used
for to reduce accidents and saves life. We have to expand
the classification accuracy of road traffic accidents types; data quality has
to be added.

In paper 10 describes
about a method called Innovators Marketplace on Data Jackets. Innovators
Marketplace on Data Jackets used to externalize the value of data through ally.
For analyzing the rate of traffic accidents on urban area   methods such as factor analysis, structure
equation modeling and data mining are used here. To construct traffic accident
risk evaluation model different indexes such as total number of accidents
reported, fatality rate injury rate   are
combined. To identify the connection between different factors population
structure information, vehicle information, road characters are used. In Here
we focused on urban data, applied structural equation modeling to find out the important
factors associated with traffic accident.  Important  
factors are   population structure,
vehicle information, structure of road etc. This paper describes six factors by
constructing an accident risk causal framework based on urban data and the component
factor sets of each feature and influence on traffic accident.

Reference

1 Analysis
of Road Traffic Fatal Accidents Using Data Mining Techniques

Liling Li,
Sharad Shrestha, Gongzhu Hu

2 Analysing road accident data using association
rule mining

Sachin Kumar; Durga Toshniwa

3
Black Spot and Accidental Attributes Identification on State Highways and
Ordinary District Roads Using Data Mining Techniques.

Gagandeep Kaur
Department of Computer Engineering Punjabi University Patiala Punjab, India
Harpreet Kaur Department of Computer Engineering Punjabi University Patiala
Punjab, India

8Traffic Accident Report Analysis using Data
Mining Techniques

Mrs. Kanchan Gawande1 Ambikesh Pandey

9 A Radical Approach
to Forecast the Road AccidentUsing Data Mining Technique

Anupama Makkar ,Harpreet
Singh Gill

 

 

 

 

 

 

 

 

 

 

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